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Genetic predisposition to obesity is associated with insulin secretion in Chinese adults: The Cardiometabolic Risk in Chinese (CRC) study
Journal of Diabetes and Its Complications 30 (2016) 1229–1233
Contents lists available at ScienceDirect
Journal of Diabetes and Its Complications j o u r n a l h o m e p a g e : W W W. J D C J O U R N A L . C O M
Genetic predisposition to obesity is associated with insulin secretion in Chinese adults: The Cardiometabolic Risk in Chinese (CRC) study Jun Liang a, b,⁎, 1, Yuting Sun c, 1, Xuekui Liu a, b, Yan Zhu c, Ying Pei d, Yu Wang a, b, Qinqin Qiu c, Manqing Yang a, b, Lu Qi e, f,⁎⁎ a
Department of Endocrinology of Xuzhou Central Hospital, Xuzhou Institute of Medical Sciences, Xuzhou Institute of Diabetes, Jiangsu 221009, China Xuzhou Clinical School of Xuzhou Medical College, Xuzhou Central Hospital Affiliated to Nanjing University of Chinese Medicine, Affiliated Hospital of Southeast University, Xuzhou, Jiangsu 221009, China c Xuzhou Medical College, Jiangsu 221000, China d School of Medicine, Southeast University, Jiangsu 210009, China e Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70118, United States f Department of Nutrition, Harvard School of Public Health, Boston, MA 02115, United States b
a r t i c l e
i n f o
Article history: Received 3 March 2016 Received in revised form 3 May 2016 Accepted 6 June 2016 Available online 11 June 2016 Keywords: Obesity-associated genetic variants Genetic risk score HOMA-B BMI Modification effects
a b s t r a c t Aims: The etiological role of obesity in determining diabetes risk among Asians may be different from that among Caucasians. The current study aimed to investigate the association between genetic predisposition to obesity and measures of insulin secretion and resistance in a large Chinese cohort. Methods: Study samples were from a community-based health examination survey in central China. A total of 2058 subjects with available biomarkers levels were included in the present study. A genetic risk score (GRS) of obesity was derived on the basis of thirteen Asian-specific body mass index (BMI)-associated variants. Results: High obesity GRS was significantly associated with increased homeostasis model assessment (HOMA)-B score (β = 7.309; P = 0.001) but not related to measures of insulin resistance. Adjustment for age, sex, BMI, and levels of lipids did not appreciably change the results. In addition, we found significant interactions between the obesity GRS and measures of body fat distribution including waist circumference (WC; P for interaction = 0.004) and neck circumference (NC; P for interaction = 0.014) on HOMA-B score. Conclusions: Our results suggest that genetic predisposition to obesity may affect beta cell function in Chinese; and body fat distribution may modify the genetic effects. © 2016 Elsevier Inc. All rights reserved.
1. Introduction Obesity is a major risk factor for type 2 diabetes in different ethnicity (Kahn, Cooper, & Del Prato, 2014; Kong et al., 2014; Scheen & Van Gaal, 2014; Stevens, Truesdale, Katz, & Cai, 2008). Of note, the relation between obesity and diabetes risk shows distinct patterns in different populations such as Caucasians and in Asians. Compared to Caucasians, Asians tend to develop diabetes with a lesser degree of obesity (Yoon et al., 2006), suggesting that overall adiposity and fat distribution may play different roles in development of diabetes Conflicts of interest: None. ⁎ Correspondence to: J. Liang, Department of Endocrinology, Xuzhou Central Hospital, 199# South Jiefang Road, Xuzhou, Jiangsu 221009, China. Tel.: +86 18952171209; fax: +86 83840486. ⁎⁎ Correspondence to: L. Qi, Harvard T.H. Chan School of Public Health, Boston, MA, United States. Tel.: +1 617 432 4116; fax: +1 617 432 2435. E-mail addresses: [email protected] (J. Liang), [email protected], [email protected] (L. Qi). 1 These authors contributed equally to this work. http://dx.doi.org/10.1016/j.jdiacomp.2016.06.006 1056-8727/© 2016 Elsevier Inc. All rights reserved.
across these populations. However, little is known about the potential mechanisms underlying such population disparity. In the past few years, genome-wide association studies have identified a group of genetic variants related to obesity. A group of studies in Caucasians suggest that the genetic susceptibility to obesity leads to increased risk of type 2 diabetes through its obesity-increasing effect, especially through insulin resistance (Dupuis et al., 2010; Goumidi, Cottel, Dallongeville, Amouyel, & Meirhaeghe, 2014; Rask-Andersen et al., 2014; Zhao, Deliard, Aziz, & Grant, 2012). However, recent studies in Asians have shown that BMI-associated loci might be related to type 2 diabetes independently of BMI (Li, Katashima, Yasumasu, & Li, 2012; Li et al., 2012; Li et al., 2012; Zhu et al., 2014). Such discrepancy motivates the interest to further investigate whether the BMI-associated loci may affect type 2 diabetes through other mechanisms in Asian populations. In the present study, we examined the associations of genetic predisposition to obesity, represented by a genetic risk score (GRS) derived from obesity-associated genetic variants identified in Asians (Wen et al., 2012), with markers related to the two major
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pathophysiological pathways for type 2 diabetes – insulin resistance and insulin secretion – in a large cohort of Chinese adults. In addition, we particularly assessed the modification effects of measures of body fat distribution, waist (WC) and neck circumference (NC), on these associations. 2. Materials and methods 2.1. Study population In 2012–2013, we conducted a community-based health examination survey in individuals who were randomly selected from 2799 residents of the urban area of central China. Written consent was obtained from all participants. The study was reviewed and approved by the ethics committee of the Central Hospital of Xuzhou and the Affiliated Hospital of Medical School of Southeast University, China. Inclusion in the present study was based on availability of: (1) DNA samples for genotyping and (2) complete oral glucose tolerance test (OGTT) or standard steamed bread meal test data. Exclusion criteria included a history of diabetes mellitus, hypertension, hyperlipidemia, heart failure, coronary artery disease, stroke, chronic renal failure, chronic liver disease, and hematological abnormalities. Individuals who were on medication affecting glucose and lipid metabolism or had less than 13 single nucleotide polymorphisms (SNPs) genotyped were also excluded. The final analyses included 2058 men and women. There were no significant differences in basic characteristics between participants with and without genotyping data. 2.2. Assessment of biomarker levels and covariates Levels of biomarkers were measured in all the participants. Venous blood sampling was performed after fasting overnight (8–12 h). After blood was drawn, samples were allowed to clot at room temperature for 1–3 h. Serum was separated immediately after clotting by centrifugation for 15 min at 3000 rpm. The blood samples were collected for measurement of serum uric acid, fasting blood glucose, serum insulin, total cholesterol (TC), total triglycerides (TG), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) levels using an autoanalyzer (Type 7600; Hitachi Ltd, Tokyo, Japan). Participants underwent a 75-g OGTT or standard steamed bread meal test. Blood samples were drawn at 120 min after the glucose or carbohydrate load. Insulin resistance was estimated according to the homeostasis model assessment (HOMA) using the following formula: HOMA-IR = fasting insulin (mU/l) × fasting glucose (mmol/l)/22.5, whereas HOMA scores for beta cell function were calculated according to the following formula: HOMA-B = [20 × fasting insulin (mU/l)]/ [fasting glucose (mmol/l) − 3.5] (Matthews et al., 1985).
Body weight was recorded to the nearest 0.1 kg with the subjects wearing light indoor clothing and no shoes. Height was recorded to the nearest 0.5 cm without shoes using a standardized wall-mounted height board. Body mass index (BMI) was calculated as weight (in kilograms) divided by the square of height (in meters). WC, an index of total abdominal fat, was measured at the mid-point between the lowest rib margin and the iliac crest (Bose et al., 2013). NC, an index of upper-body fat, was measured with a flexible tape in a standardized manner horizontally above the cricothyroid cartilage to 1-mm accuracy (Zen et al., 2012). Blood pressure (BP) was measured by trained doctors using a mercury sphygmomanometer on the dominant arm after a resting period of at least 5 min in the supine position (Tan et al., 2003; Tso et al., 2006). The subject's arm was placed at the heart level, and BP values were taken as the mean of 3 measurements. Central obesity was determined based on WC (≥85 cm for men and ≥ 80 cm for women) (Zhou & Cooperative Meta-Analysis Group of the Working Group on Obesity in China, 2002). 2.3. Genotyping Genomic DNA was extracted from the buffy coat fraction of centrifuged blood using a QIAmp Blood Mini Kit (Qiagen, Chatsworth, CA). We selected 13 SNPs from 13 genetic loci (Table S1), which were identified as being associated with BMI by previous genome-wide association studies (GWAS) in East Asian populations (Wen et al., 2012). All SNPs were detected using Taqman SNP allelic discrimination with an ABI 7900HT system (Applied Biosystems, Foster City, CA), and the genotyping success rate was 99%. Replicate quality-control samples (10%) were included and genotyped with greater than 99% concordance. We calculated the GRS of each individual as the number of alleles associated with increased BMI among the 13 SNPs. 2.4. Statistical analysis Data management and statistical analysis were performed using SAS statistical software (version 9.1; SAS Institute, Inc., Cary, NC, USA). Hardy–Weinberg equilibrium was verified and genotype and allele frequencies were compared using the χ 2 test. Differences in continuous variables at baseline were tested using one-way analysis of variance (ANOVA). Data are presented as mean ± standard deviation (SD). Relations between GRS and glycemic traits were examined using multivariate linear regression models. We adjusted for multiple potential confounding variables in the models. All reported P-values are two-tailed. The level of statistical significance was set at P b 0.05.
Table 1 Baseline characteristics of the study participants according to GRS. Variables
Genetic risk score (in tertiles) T1 (2–9)
N Age (years) BMI (kg/m2) Neck circumference (cm) Waist circumference (cm) Body fat rate (%) Systolic blood pressure (mm Hg) Diastolic blood pressure (mm Hg) Biomarkers TC (mmol/l) TG (mmol/l) HDL-C (mmol/l) LDL-C (mmol/l)
1012 46.31 24.39 36.40 86.21 26.45 124.55 79.57
± ± ± ± ± ± ±
9.24 2.91 3.37 9.42 4.99 15.73 11.49
5.09 1.74 1.24 3.03
± ± ± ±
0.91 1.74 0.29 0.78
T2 (10–11)
T3 (12–17)
602 45.42 24.45 36.59 86.25 26.47 124.16 79.07
± ± ± ± ± ± ±
8.87 3.11 3.33 9.59 4.93 15.76 11.15
444 45.82 24.99 36.80 87.40 26.92 124.34 80.04
± ± ± ± ± ± ±
8.54 2.92 4.52 9.09 4.70 15.19 11.18
5.04 1.77 1.21 3.00
± ± ± ±
0.89 1.63 0.29 0.76
5.03 1.78 1.21 3.00
± ± ± ±
0.87 1.58 0.30 0.77
Abbreviations: BMI, body mass index; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.
P for trend
0.341 b0.001 0.045 0.029 0.096 0.818 0.462 0.594 0.242 0.196 0.456
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3. Results 3.1. Baseline characteristics of the study participants according to obesity GRS (in tertiles) The clinical characteristics of the study population according to obesity GRS are shown in Table 1. BMI, WC, and NC were significantly different across the tertiles of obesity GRS, with a trend for increasing values with increasing GRSs. There were no significant differences in other variables across the GRSs. 3.2. Associations of obesity GRS with insulin secretion and insulin resistance Table 2 displays the associations of obesity GRS with measures of insulin secretion and insulin resistance. A significant association was observed between GRS and HOMA-B score. We found that a 1-unit increase of GRS was associated with a 7.309-unit increase of HOMA-B score (P = 0.001) in the crude model (Fig. 1). Further adjustment for age, sex, levels of lipids, systolic blood pressure (SBP), and diastolic blood pressure (DBP) did not significantly change the observed association (β = 5.985; P = 0.006). Furthermore, the relationship remained significant after adjusting for BMI (β = 4.285; P = 0.021). The GRS was not related to other measures. 3.3. Modifications of body fat distribution on the association between the obesity GRS and HOMA-B score We next examined whether body fat distribution modified the relation between obesity GRS and HOMA-B score (Table 3 and Fig. 2). Both WC and NC significantly interacted with GRS on HOMA-B score (P for interaction = 0.004 and 0.014, respectively). We found that the association between GRS and HOMA-B score was more significant among individuals with greater WC (≥ 85 cm in men and ≥ 80 cm in women, P for trend = 0.008) than those with smaller WC (b 85 cm in men and b 80 cm in women, P for trend = 0.174). In addition, the association between GRS and HOMA-B score was more significant among those with medium (35.5 b NC b 38 cm, P for trend = 0.046) and high (NC ≥ 38 cm, P for trend = 0.018) NC than those with low NC (b 35.5 cm, P for trend = 0.426). 4. Discussion In the present study, we found a significant association between the Asian-specific obesity GRS and the HOMA-B score in a large cohort of non-diabetic Chinese adults after adjusting for BMI and other metabolic markers. Our findings are in line with the results of some previous studies in Chinese, in which the obesity-predisposing alleles were associated Table 2 Associations of genetic risk score (GRS) with measures of insulin secretion and insulin resistance. Variable
FBG (mmol/l) 2-h glucose (mmol/l) FINS HOMA-B (%) HOMA-IR (%)
Model 1
Model 2
Model 3
Beta
P
Beta
P
Beta
P
−0.004 0.068 0.311 7.309 0.065
0.909 0.472 0.085 0.001 0.243
0.004 0.101 0.236 5.985 0.050
0.901 0.266 0.159 0.006 0.341
0.005 0.086 0.088 4.285 0.014
0.886 0.596 0.331 0.021 0.790
Abbreviations: FBG, fasting blood glucose; FINS, fasting blood insulin; HOMA, homeostasis model assessment; IR, insulin resistance. Model 1: unadjusted; Model 2: adjusted for sex, age, systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol level (TC), triglycerides level (TG), high-density lipoprotein level (HDL), and low-density lipoprotein level (LDL); Model 3: adjusted for sex, age, SBP, DBP, TC, TG, HDL, LDL, and body mass index.
Fig. 1. The distribution of the number of alleles associated with increased body mass index (BMI) in the population. The histograms represent the number of the participants, and the mean values ± SE of homeostasis model assessment (HOMA)-B score are plotted with the trend lines across the genetic risk score.
with markers of beta cell function after adjustment for BMI (Zhu et al., 2014). These results are distinct from the observations reported in Caucasians, in whom the obesity-related loci were primarily associated with insulin resistance (Dupuis et al., 2010; Goumidi et al., 2014; Rask-Andersen et al., 2014; Zhao et al., 2012). Such a discrepancy between Asians and Caucasians might reflect the difference in etiological role of obesity in the development of diabetes, which has been noted for a long time (Davis, Coleman, Holman, & UKPDS Group, 2014; Zhu et al., 2014). Although findings from the previous GWAS emphasized the importance of beta cell function in the development of type 2 diabetes (Meier & Bonadonna, 2013), the exact mechanisms underlying the effects of the obesity related-loci on beta cell function remain unknown. According to the thrifty gene hypothesis, the periods of famine in the history of humans presented a significant evolutionary force favoring the selection of genes involved in fat storage and responsible for efficient release of massive amounts of insulin to increase fertility and survival (Prentice, Hennig, & Fulford, 2008). In modern societies, the thrifty genes continue to prepare their hosts to famines that do not occur, which leads to obesity and increases the risk to type 2 diabetes (Speakman, 2008). Our findings lend support to the view that, at least in Asians, the obesity-associated loci may also affect beta cell function and secretion of insulin, which are the key etiological changes in the development of diabetes. Intriguingly, we found that measures of body fat distribution such as NC and WC might modify the relation between the obesity-associated genotype and HOMA-B score. Compared with Caucasians, Asians have more visceral adipose tissue (Li et al., 2012; Li et al., 2012), and visceral obesity is the predominant obesity type in
Table 3 Stratified associations between genetic risk score (GRS) and homeostasis model assessment (HOMA)-B score by measures of body fat distribution. Variable
Stratification
Beta
S.E.
P for trend
P for interaction
WC a (cm)
Male b85, female b80 Male ≥85, female ≥80 b35.5 ≥35.5, b38 ≥38
1.410 2.707 0.878 2.707 3.303
1.036 1.020 1.103 1.355 1.392
0.174 0.008 0.426 0.046 0.018
0.004
NC
b
(cm)
0.014
Abbreviations: WC, waist circumference; NC, neck circumference. Adjusted for sex, age, body mass index, systolic blood pressure, diastolic blood pressure, total cholesterol level, triglycerides level, high-density lipoprotein level, and low-density lipoprotein level. a Based on the diagnostic criteria for Chinese population. b Data were calculated for tertiles.
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5. Conclusion In conclusion, the findings of the present study indicate that the predisposition to obesity may affect beta cell function and insulin secretion in Chinese; and body fat distribution may modify the genetic effects. Our findings lend support to the distinct roles of obesity in the development of diabetes between Asians and Caucasians. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.jdiacomp.2016.06.006. Funding This work was supported by (1) the Jiangsu Provincial Bureau of Health Foundation (Grant No. H201356), (2) Jiangsu Six Talent Peaks Program (No. 2013-WSN-013) and The 4th “333” scientific research project of Jiangsu Province (BRA2014058), and (3) Xuzhou Outstanding Medical Academic Leader Project and Xuzhou Science and Technology Grants (Nos. XM13B066 and KC14SX013). References
Fig. 2. The joint association of genetic risk score (in tertiles) and neck circumference (a) and waist circumference (b) with the homeostasis model assessment (HOMA)-B.
Chinese. Previous studies have revealed that visceral adipose tissue has high lipogenesis and lipolysis activities, and its accumulation increases the concentration of free fatty acids in portal circulation, which is directly connected to the liver (Funahashi et al., 1999; Wajchenberg, 2000). The excess of free fatty acids may lead to enhancement of lipid synthesis and gluconeogenesis, resulting in hyperlipidemia, glucose intolerance, hypertension, and atherosclerosis (Bjöntorp, 2000; Matsuzawa, Shimomura, Nakamura, Keno, & Tokunaga, 1994; Wajchenberg, 2000). NC, an index of upper-body fat, has also been associated with the majority of systemic free fatty acid release (Liang et al., 2014; Liang et al., 2014). In addition, high NC has been found to be a significant predictor of obstructive sleep apnea syndrome (OSAS), which has been associated with aggravated glycemic control, even at the earliest stages of glucose intolerance (Liang et al., 2014; Liang et al., 2014; Liang et al., 2013). This effect of WC and NC on glucose homeostasis regulation could influence insulin secretion. Our data suggest that the obesity-related genotype, visceral and upper-body fat distribution may play complementary and synergistic roles in determining diabetes risk. Several limitations of this study warrant consideration. First, we only analyzed the obesity-related GRS but not individual variants. Our study might be under-powered to detect the effects of individual genetic variants on metabolic measures. However, the GRS has been widely employed in studying their relation with metabolic outcomes (Qi et al., 2012). Second, among the Asian populations, our study only included Chinese. Further studies are warranted to validate our findings in other Asian populations.
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Contents lists available at ScienceDirect
Journal of Diabetes and Its Complications j o u r n a l h o m e p a g e : W W W. J D C J O U R N A L . C O M
Genetic predisposition to obesity is associated with insulin secretion in Chinese adults: The Cardiometabolic Risk in Chinese (CRC) study Jun Liang a, b,⁎, 1, Yuting Sun c, 1, Xuekui Liu a, b, Yan Zhu c, Ying Pei d, Yu Wang a, b, Qinqin Qiu c, Manqing Yang a, b, Lu Qi e, f,⁎⁎ a
Department of Endocrinology of Xuzhou Central Hospital, Xuzhou Institute of Medical Sciences, Xuzhou Institute of Diabetes, Jiangsu 221009, China Xuzhou Clinical School of Xuzhou Medical College, Xuzhou Central Hospital Affiliated to Nanjing University of Chinese Medicine, Affiliated Hospital of Southeast University, Xuzhou, Jiangsu 221009, China c Xuzhou Medical College, Jiangsu 221000, China d School of Medicine, Southeast University, Jiangsu 210009, China e Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70118, United States f Department of Nutrition, Harvard School of Public Health, Boston, MA 02115, United States b
a r t i c l e
i n f o
Article history: Received 3 March 2016 Received in revised form 3 May 2016 Accepted 6 June 2016 Available online 11 June 2016 Keywords: Obesity-associated genetic variants Genetic risk score HOMA-B BMI Modification effects
a b s t r a c t Aims: The etiological role of obesity in determining diabetes risk among Asians may be different from that among Caucasians. The current study aimed to investigate the association between genetic predisposition to obesity and measures of insulin secretion and resistance in a large Chinese cohort. Methods: Study samples were from a community-based health examination survey in central China. A total of 2058 subjects with available biomarkers levels were included in the present study. A genetic risk score (GRS) of obesity was derived on the basis of thirteen Asian-specific body mass index (BMI)-associated variants. Results: High obesity GRS was significantly associated with increased homeostasis model assessment (HOMA)-B score (β = 7.309; P = 0.001) but not related to measures of insulin resistance. Adjustment for age, sex, BMI, and levels of lipids did not appreciably change the results. In addition, we found significant interactions between the obesity GRS and measures of body fat distribution including waist circumference (WC; P for interaction = 0.004) and neck circumference (NC; P for interaction = 0.014) on HOMA-B score. Conclusions: Our results suggest that genetic predisposition to obesity may affect beta cell function in Chinese; and body fat distribution may modify the genetic effects. © 2016 Elsevier Inc. All rights reserved.
1. Introduction Obesity is a major risk factor for type 2 diabetes in different ethnicity (Kahn, Cooper, & Del Prato, 2014; Kong et al., 2014; Scheen & Van Gaal, 2014; Stevens, Truesdale, Katz, & Cai, 2008). Of note, the relation between obesity and diabetes risk shows distinct patterns in different populations such as Caucasians and in Asians. Compared to Caucasians, Asians tend to develop diabetes with a lesser degree of obesity (Yoon et al., 2006), suggesting that overall adiposity and fat distribution may play different roles in development of diabetes Conflicts of interest: None. ⁎ Correspondence to: J. Liang, Department of Endocrinology, Xuzhou Central Hospital, 199# South Jiefang Road, Xuzhou, Jiangsu 221009, China. Tel.: +86 18952171209; fax: +86 83840486. ⁎⁎ Correspondence to: L. Qi, Harvard T.H. Chan School of Public Health, Boston, MA, United States. Tel.: +1 617 432 4116; fax: +1 617 432 2435. E-mail addresses: [email protected] (J. Liang), [email protected], [email protected] (L. Qi). 1 These authors contributed equally to this work. http://dx.doi.org/10.1016/j.jdiacomp.2016.06.006 1056-8727/© 2016 Elsevier Inc. All rights reserved.
across these populations. However, little is known about the potential mechanisms underlying such population disparity. In the past few years, genome-wide association studies have identified a group of genetic variants related to obesity. A group of studies in Caucasians suggest that the genetic susceptibility to obesity leads to increased risk of type 2 diabetes through its obesity-increasing effect, especially through insulin resistance (Dupuis et al., 2010; Goumidi, Cottel, Dallongeville, Amouyel, & Meirhaeghe, 2014; Rask-Andersen et al., 2014; Zhao, Deliard, Aziz, & Grant, 2012). However, recent studies in Asians have shown that BMI-associated loci might be related to type 2 diabetes independently of BMI (Li, Katashima, Yasumasu, & Li, 2012; Li et al., 2012; Li et al., 2012; Zhu et al., 2014). Such discrepancy motivates the interest to further investigate whether the BMI-associated loci may affect type 2 diabetes through other mechanisms in Asian populations. In the present study, we examined the associations of genetic predisposition to obesity, represented by a genetic risk score (GRS) derived from obesity-associated genetic variants identified in Asians (Wen et al., 2012), with markers related to the two major
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pathophysiological pathways for type 2 diabetes – insulin resistance and insulin secretion – in a large cohort of Chinese adults. In addition, we particularly assessed the modification effects of measures of body fat distribution, waist (WC) and neck circumference (NC), on these associations. 2. Materials and methods 2.1. Study population In 2012–2013, we conducted a community-based health examination survey in individuals who were randomly selected from 2799 residents of the urban area of central China. Written consent was obtained from all participants. The study was reviewed and approved by the ethics committee of the Central Hospital of Xuzhou and the Affiliated Hospital of Medical School of Southeast University, China. Inclusion in the present study was based on availability of: (1) DNA samples for genotyping and (2) complete oral glucose tolerance test (OGTT) or standard steamed bread meal test data. Exclusion criteria included a history of diabetes mellitus, hypertension, hyperlipidemia, heart failure, coronary artery disease, stroke, chronic renal failure, chronic liver disease, and hematological abnormalities. Individuals who were on medication affecting glucose and lipid metabolism or had less than 13 single nucleotide polymorphisms (SNPs) genotyped were also excluded. The final analyses included 2058 men and women. There were no significant differences in basic characteristics between participants with and without genotyping data. 2.2. Assessment of biomarker levels and covariates Levels of biomarkers were measured in all the participants. Venous blood sampling was performed after fasting overnight (8–12 h). After blood was drawn, samples were allowed to clot at room temperature for 1–3 h. Serum was separated immediately after clotting by centrifugation for 15 min at 3000 rpm. The blood samples were collected for measurement of serum uric acid, fasting blood glucose, serum insulin, total cholesterol (TC), total triglycerides (TG), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) levels using an autoanalyzer (Type 7600; Hitachi Ltd, Tokyo, Japan). Participants underwent a 75-g OGTT or standard steamed bread meal test. Blood samples were drawn at 120 min after the glucose or carbohydrate load. Insulin resistance was estimated according to the homeostasis model assessment (HOMA) using the following formula: HOMA-IR = fasting insulin (mU/l) × fasting glucose (mmol/l)/22.5, whereas HOMA scores for beta cell function were calculated according to the following formula: HOMA-B = [20 × fasting insulin (mU/l)]/ [fasting glucose (mmol/l) − 3.5] (Matthews et al., 1985).
Body weight was recorded to the nearest 0.1 kg with the subjects wearing light indoor clothing and no shoes. Height was recorded to the nearest 0.5 cm without shoes using a standardized wall-mounted height board. Body mass index (BMI) was calculated as weight (in kilograms) divided by the square of height (in meters). WC, an index of total abdominal fat, was measured at the mid-point between the lowest rib margin and the iliac crest (Bose et al., 2013). NC, an index of upper-body fat, was measured with a flexible tape in a standardized manner horizontally above the cricothyroid cartilage to 1-mm accuracy (Zen et al., 2012). Blood pressure (BP) was measured by trained doctors using a mercury sphygmomanometer on the dominant arm after a resting period of at least 5 min in the supine position (Tan et al., 2003; Tso et al., 2006). The subject's arm was placed at the heart level, and BP values were taken as the mean of 3 measurements. Central obesity was determined based on WC (≥85 cm for men and ≥ 80 cm for women) (Zhou & Cooperative Meta-Analysis Group of the Working Group on Obesity in China, 2002). 2.3. Genotyping Genomic DNA was extracted from the buffy coat fraction of centrifuged blood using a QIAmp Blood Mini Kit (Qiagen, Chatsworth, CA). We selected 13 SNPs from 13 genetic loci (Table S1), which were identified as being associated with BMI by previous genome-wide association studies (GWAS) in East Asian populations (Wen et al., 2012). All SNPs were detected using Taqman SNP allelic discrimination with an ABI 7900HT system (Applied Biosystems, Foster City, CA), and the genotyping success rate was 99%. Replicate quality-control samples (10%) were included and genotyped with greater than 99% concordance. We calculated the GRS of each individual as the number of alleles associated with increased BMI among the 13 SNPs. 2.4. Statistical analysis Data management and statistical analysis were performed using SAS statistical software (version 9.1; SAS Institute, Inc., Cary, NC, USA). Hardy–Weinberg equilibrium was verified and genotype and allele frequencies were compared using the χ 2 test. Differences in continuous variables at baseline were tested using one-way analysis of variance (ANOVA). Data are presented as mean ± standard deviation (SD). Relations between GRS and glycemic traits were examined using multivariate linear regression models. We adjusted for multiple potential confounding variables in the models. All reported P-values are two-tailed. The level of statistical significance was set at P b 0.05.
Table 1 Baseline characteristics of the study participants according to GRS. Variables
Genetic risk score (in tertiles) T1 (2–9)
N Age (years) BMI (kg/m2) Neck circumference (cm) Waist circumference (cm) Body fat rate (%) Systolic blood pressure (mm Hg) Diastolic blood pressure (mm Hg) Biomarkers TC (mmol/l) TG (mmol/l) HDL-C (mmol/l) LDL-C (mmol/l)
1012 46.31 24.39 36.40 86.21 26.45 124.55 79.57
± ± ± ± ± ± ±
9.24 2.91 3.37 9.42 4.99 15.73 11.49
5.09 1.74 1.24 3.03
± ± ± ±
0.91 1.74 0.29 0.78
T2 (10–11)
T3 (12–17)
602 45.42 24.45 36.59 86.25 26.47 124.16 79.07
± ± ± ± ± ± ±
8.87 3.11 3.33 9.59 4.93 15.76 11.15
444 45.82 24.99 36.80 87.40 26.92 124.34 80.04
± ± ± ± ± ± ±
8.54 2.92 4.52 9.09 4.70 15.19 11.18
5.04 1.77 1.21 3.00
± ± ± ±
0.89 1.63 0.29 0.76
5.03 1.78 1.21 3.00
± ± ± ±
0.87 1.58 0.30 0.77
Abbreviations: BMI, body mass index; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.
P for trend
0.341 b0.001 0.045 0.029 0.096 0.818 0.462 0.594 0.242 0.196 0.456
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3. Results 3.1. Baseline characteristics of the study participants according to obesity GRS (in tertiles) The clinical characteristics of the study population according to obesity GRS are shown in Table 1. BMI, WC, and NC were significantly different across the tertiles of obesity GRS, with a trend for increasing values with increasing GRSs. There were no significant differences in other variables across the GRSs. 3.2. Associations of obesity GRS with insulin secretion and insulin resistance Table 2 displays the associations of obesity GRS with measures of insulin secretion and insulin resistance. A significant association was observed between GRS and HOMA-B score. We found that a 1-unit increase of GRS was associated with a 7.309-unit increase of HOMA-B score (P = 0.001) in the crude model (Fig. 1). Further adjustment for age, sex, levels of lipids, systolic blood pressure (SBP), and diastolic blood pressure (DBP) did not significantly change the observed association (β = 5.985; P = 0.006). Furthermore, the relationship remained significant after adjusting for BMI (β = 4.285; P = 0.021). The GRS was not related to other measures. 3.3. Modifications of body fat distribution on the association between the obesity GRS and HOMA-B score We next examined whether body fat distribution modified the relation between obesity GRS and HOMA-B score (Table 3 and Fig. 2). Both WC and NC significantly interacted with GRS on HOMA-B score (P for interaction = 0.004 and 0.014, respectively). We found that the association between GRS and HOMA-B score was more significant among individuals with greater WC (≥ 85 cm in men and ≥ 80 cm in women, P for trend = 0.008) than those with smaller WC (b 85 cm in men and b 80 cm in women, P for trend = 0.174). In addition, the association between GRS and HOMA-B score was more significant among those with medium (35.5 b NC b 38 cm, P for trend = 0.046) and high (NC ≥ 38 cm, P for trend = 0.018) NC than those with low NC (b 35.5 cm, P for trend = 0.426). 4. Discussion In the present study, we found a significant association between the Asian-specific obesity GRS and the HOMA-B score in a large cohort of non-diabetic Chinese adults after adjusting for BMI and other metabolic markers. Our findings are in line with the results of some previous studies in Chinese, in which the obesity-predisposing alleles were associated Table 2 Associations of genetic risk score (GRS) with measures of insulin secretion and insulin resistance. Variable
FBG (mmol/l) 2-h glucose (mmol/l) FINS HOMA-B (%) HOMA-IR (%)
Model 1
Model 2
Model 3
Beta
P
Beta
P
Beta
P
−0.004 0.068 0.311 7.309 0.065
0.909 0.472 0.085 0.001 0.243
0.004 0.101 0.236 5.985 0.050
0.901 0.266 0.159 0.006 0.341
0.005 0.086 0.088 4.285 0.014
0.886 0.596 0.331 0.021 0.790
Abbreviations: FBG, fasting blood glucose; FINS, fasting blood insulin; HOMA, homeostasis model assessment; IR, insulin resistance. Model 1: unadjusted; Model 2: adjusted for sex, age, systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol level (TC), triglycerides level (TG), high-density lipoprotein level (HDL), and low-density lipoprotein level (LDL); Model 3: adjusted for sex, age, SBP, DBP, TC, TG, HDL, LDL, and body mass index.
Fig. 1. The distribution of the number of alleles associated with increased body mass index (BMI) in the population. The histograms represent the number of the participants, and the mean values ± SE of homeostasis model assessment (HOMA)-B score are plotted with the trend lines across the genetic risk score.
with markers of beta cell function after adjustment for BMI (Zhu et al., 2014). These results are distinct from the observations reported in Caucasians, in whom the obesity-related loci were primarily associated with insulin resistance (Dupuis et al., 2010; Goumidi et al., 2014; Rask-Andersen et al., 2014; Zhao et al., 2012). Such a discrepancy between Asians and Caucasians might reflect the difference in etiological role of obesity in the development of diabetes, which has been noted for a long time (Davis, Coleman, Holman, & UKPDS Group, 2014; Zhu et al., 2014). Although findings from the previous GWAS emphasized the importance of beta cell function in the development of type 2 diabetes (Meier & Bonadonna, 2013), the exact mechanisms underlying the effects of the obesity related-loci on beta cell function remain unknown. According to the thrifty gene hypothesis, the periods of famine in the history of humans presented a significant evolutionary force favoring the selection of genes involved in fat storage and responsible for efficient release of massive amounts of insulin to increase fertility and survival (Prentice, Hennig, & Fulford, 2008). In modern societies, the thrifty genes continue to prepare their hosts to famines that do not occur, which leads to obesity and increases the risk to type 2 diabetes (Speakman, 2008). Our findings lend support to the view that, at least in Asians, the obesity-associated loci may also affect beta cell function and secretion of insulin, which are the key etiological changes in the development of diabetes. Intriguingly, we found that measures of body fat distribution such as NC and WC might modify the relation between the obesity-associated genotype and HOMA-B score. Compared with Caucasians, Asians have more visceral adipose tissue (Li et al., 2012; Li et al., 2012), and visceral obesity is the predominant obesity type in
Table 3 Stratified associations between genetic risk score (GRS) and homeostasis model assessment (HOMA)-B score by measures of body fat distribution. Variable
Stratification
Beta
S.E.
P for trend
P for interaction
WC a (cm)
Male b85, female b80 Male ≥85, female ≥80 b35.5 ≥35.5, b38 ≥38
1.410 2.707 0.878 2.707 3.303
1.036 1.020 1.103 1.355 1.392
0.174 0.008 0.426 0.046 0.018
0.004
NC
b
(cm)
0.014
Abbreviations: WC, waist circumference; NC, neck circumference. Adjusted for sex, age, body mass index, systolic blood pressure, diastolic blood pressure, total cholesterol level, triglycerides level, high-density lipoprotein level, and low-density lipoprotein level. a Based on the diagnostic criteria for Chinese population. b Data were calculated for tertiles.
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5. Conclusion In conclusion, the findings of the present study indicate that the predisposition to obesity may affect beta cell function and insulin secretion in Chinese; and body fat distribution may modify the genetic effects. Our findings lend support to the distinct roles of obesity in the development of diabetes between Asians and Caucasians. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.jdiacomp.2016.06.006. Funding This work was supported by (1) the Jiangsu Provincial Bureau of Health Foundation (Grant No. H201356), (2) Jiangsu Six Talent Peaks Program (No. 2013-WSN-013) and The 4th “333” scientific research project of Jiangsu Province (BRA2014058), and (3) Xuzhou Outstanding Medical Academic Leader Project and Xuzhou Science and Technology Grants (Nos. XM13B066 and KC14SX013). References
Fig. 2. The joint association of genetic risk score (in tertiles) and neck circumference (a) and waist circumference (b) with the homeostasis model assessment (HOMA)-B.
Chinese. Previous studies have revealed that visceral adipose tissue has high lipogenesis and lipolysis activities, and its accumulation increases the concentration of free fatty acids in portal circulation, which is directly connected to the liver (Funahashi et al., 1999; Wajchenberg, 2000). The excess of free fatty acids may lead to enhancement of lipid synthesis and gluconeogenesis, resulting in hyperlipidemia, glucose intolerance, hypertension, and atherosclerosis (Bjöntorp, 2000; Matsuzawa, Shimomura, Nakamura, Keno, & Tokunaga, 1994; Wajchenberg, 2000). NC, an index of upper-body fat, has also been associated with the majority of systemic free fatty acid release (Liang et al., 2014; Liang et al., 2014). In addition, high NC has been found to be a significant predictor of obstructive sleep apnea syndrome (OSAS), which has been associated with aggravated glycemic control, even at the earliest stages of glucose intolerance (Liang et al., 2014; Liang et al., 2014; Liang et al., 2013). This effect of WC and NC on glucose homeostasis regulation could influence insulin secretion. Our data suggest that the obesity-related genotype, visceral and upper-body fat distribution may play complementary and synergistic roles in determining diabetes risk. Several limitations of this study warrant consideration. First, we only analyzed the obesity-related GRS but not individual variants. Our study might be under-powered to detect the effects of individual genetic variants on metabolic measures. However, the GRS has been widely employed in studying their relation with metabolic outcomes (Qi et al., 2012). Second, among the Asian populations, our study only included Chinese. Further studies are warranted to validate our findings in other Asian populations.
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